The Location Strategies of Emerging Countries Multinationals in the EU Regions

1. The Location Strategies of Emerging Countries Multinationalsin the EU Regions Riccardo Crescenzi Department of Geography and Environment London School of Economics r.crescenzi@lse.ac.uk Carlo Pietrobelli Inter-American Development Bank carlop@iadb.org Roberta Rabellotti Dipartimento di Scienze Politiche e Sociali Università di Pavia roberta.rabellotti@unipv.it SPRU – 18th October 2013

2. Motivations • FDI from developing economies reached the record level of $426 billion in 2012, corresponding to 31% of global outflows, up from 16% in 2007 (UNCTAD, 2013); • There is a lot of interest in the IB literature about differences and similarities between Emerging Market Multinationals (EMNEs) and Advances Countries MNEs (AMNEs) strategies of internalization (Ramamurti and Singh, 2009); • Strictly related with the decision of internalization is the choice of appropriate locations for the subsidiaries; • The focus of this paper is on the similarities and differences of the location strategies of EMNEs and AMNEs in the EU-25 regions.

3. Location is driven by motivations(Belderbos et al 2011; Dunning 2009) • Market-seeking investments are directed to large market size countries; • Strategic-asset seeking investments go to technologically advanced countries; • Efficiency-seeking investmentsare attracted by low wage countries; • Resource-seeking investments are interested in natural resource rich countries.

4. Location isalsodriven by agglomeration • To reduce the risks attached to uncertainties for prospective location choice firms tend to match the location decisions of their competitors (Knickerbocker, 1973); • EMNEsto reduce the risks attached to investments far from home, in locations which are culturally and geographically distant, are likely to match the location decision of their competitors from the same industry; • MNEs benefit from being part of a geographical network or cluster of related activities (Dunning, 2009; Beugelsdijk and Mudambi, 2013 ).

5. MNEs location and Value Chain • In Crescenzi, Pietrobelli and Rabellotti (2013 forthcoming in JoEG) we empirically show that different drivers affect the location of investments at different stages of the value chain in the EU-25 regions; • For instance R&D investments search for different local conditions (highly qualified people, innovative regions) than new manufacturing plants (low paid unskilled labor); • In thisstudy, we focus on the location determinants of EMNEs and AMNEs and investigate whether in their decision to relocate different stages of the Value Chain they take into account diverse factors.

6. Research questions • How do investment strategies by EMNES differ from those by AMNEs? • Do EMNEs and AMNEs location strategies vary according to the VC stage undertaken through the investment project? • Are national and regional characteristics of the destination area valued differently by EMNEs and AMNEs?

7. Data sources • fDi Markets:the dataset includes approximately 72,000 greenfield investments covering all sectors and countries worldwidefrom 2003 to 2008; • Our empirical analysis is based on the 19,444 projects undertaken by MNEs from the entire world into the EU25 countries (robustness checks with UNCTAD and Euromonitor); • For each project, the dataset contains detailed information on the investor (name and state/country of origin), the destination area (country, state and city), the year of the investment; the sector and the activity undertaken; • Each investment is geocoded at the NUTS2 level with the exception of UK, BE, and DE where NUTS1 is used; • Two definitions of EMNEs: EME (India, China, Russia, Turkey, Honk Kong, Brazil, Mexico, South Africa, Thailand and Chile) and EME2 (also including Argentina, Malaysia and Ukraine).

8. Investments projects VC classification

9. Value Chains and EMNEs vs. AMNEs • The location drivers of the investments from different origins are compared across two sub-samples: production-oriented activities (MAN) and non-manufacturing activities (NON MAN) including the remaining 4 stages characterised by higher value added (HQ, INNO, SALES, LOG&DIST); • Agglomeration of investments at the same VC stage is captured by means of a specific proxy based on the cumulative number of investments at the same VC stage in the same region.

10. The NestedLogitModel • Pij is the probability of choosing a region j in a country i; • Pj/i is the probability of choosing region j conditioned on the choice of country i, depending on the characteristics of the ni regions belonging to country I; • Pi is the probability of choosing a country i depending on the characteristics of the country and on those of all its regions. • The location process involves two simultaneous decisions: a) choosing a country i and b) selecting a region j in the chosen i country.

11. Investment location drivers The probability of a certain region to be chosen as a destination of a foreign investment is estimated as a function of: • Market seeking motivation:Regional GDP pro capite; • Strategic asset seeking motivation: • Patent Intensity to capture the extent to which MNEs expect to benefit from localised knowledge spilloversfrom indigenous firms; • Social filter index • Efficiency seeking motivation: Regional unemployment as a proxy of the labour market conditions in terms of the excess of labour supply over demand; • Regional agglomeration of foreign investments: a) Total pre-existing investments; b) Investments in the same sector; c) Investments in the same VC stage.

12. ‘Social Filter’ Index (Crescenzi et al., 2007, 2012; Crescenzi and Rodrıguez-Pose, 2011) • SF is an indicator based on structural pre-conditions to establish fully functional regional systems of innovation andsocio-institutional conditions favorable to the embeddedness of economic activities; • SF includes two major domains combined through principal component analysis: • educational achievements; • productive employment of human resources; • These two domains, when assessed simultaneously, generate a socio-economic profile that make some regions prone and others averse to innovation.

13. Location of MNCs in the EU regions by area of origin

14. Location of MNCs in the EU regions by area of origin: Non-manufacturing activities(HQ, INNO, SALES, LOG&DIST)

15. DissimilarityParameters: the ‘weight’ the investorascribes to regional (1) vs national (0) drivers

16. EU10 vs EU15Non-manufacturing activities(HQ, INNO, SALES, LOG&DIST)

17. Preliminary conclusions • Agglomeration at VC stage (and at sectoral level) is a key location driver both for EMNEs and AMNEs; • Market seeking investments: intra EU vs extra EU pattern; • Strategic asset seeking: • only NON MAN EMNEs investments are attracted by patents; • Soft innovation factors (proxied by the Social Filter) are relevant only for intra-EU investments; • The national and the regional drivers play different roles in different host and home countries.

18. Thankyouroberta.rabellotti@unipv.ithttp://sites.google.com/site/robertarabellotti/homeCrescenziR., Pietrobelli, C.,Rabellotti R. (2013) Innovation Drivers, Value Chains and the Geography of Multinational Corporations in Europeforthcoming in Journal of Economic Geography

19. The ‘Social Filter’ combines, by means of Principal Component Analysis • % employed people with tertiary education level • % population with tertiary education level • Agricultural employment as % of total employment • Long term unemployed as % of total unemployment. • People aged 15-24 as % of total population

20. DissimilarityParameters: regions vs country factors (NON MAN) ERSA - Palermo - 27-30 September 2013